About DAGS

DAGS is Professor Daphne Koller's
research group. Our main research focus is on dealing with complex domains that
involve large amounts of uncertainty. Our work builds on the framework of
probability theory, decision theory, and game theory, but uses techniques from
artificial intelligence and computer science to allow us to apply this framework
to complex real-world problems.

Most of our work is based on the use of probabilistic graphical models such as
Bayesian networks, influence diagrams, and Markov decision processes. Within
that topic, our work touches on many areas: representation, inference, learning,
and decision making. One main focus has been the extension of the
representational power of the probabilistic graphical modeling language, to
encompass a much richer set of domains. For example, we have worked on:

We believe that a good representation must also support effective inference and
learning algorithms. Hence, our work is also highly focused on these topics.
We have worked on exact and approximate inference algorithms for these
representations, and on approaches for learning these models from data. On the
inference side, we have done a lot of work on inference in dynamic Bayesian
networks, inference in hybrid Bayesian
networks, decision making in factored MDPs,
and inference for large scale models such as those generated by a PRM or an
OOBN. On the learning side, we have done a lot of work on learning
probabilistic models from relational databases, on
active learning of probabilistic models (where the
learner can query for particular types of instances), and on
learning utility functions from data.

Our work spans the range from concepts to theory to applications. Some of our
work is conceptual: defining new representation schemes and exploring their
expressive power. Some of it is theoretical and algorithmic: designing new
inference and learning algorithms and proving that they achieve certain
properties. And some is applied: experimenting with our approaches on both
synthetic and real problems. Some of the applications that we are particularly
interested in right now are: learning models from rich heterogenous biomedical
databases, which can include clinical, genomic, genetic, and epidemiological
data; fault diagnosis for complex hybrid systems; and tracking at the symbolic
level from low-level visual data.